alter ’employee’, {NAME => ‘col_fam1’, COMPRESSION => ‘GZ’}
With alter command you can add columns on fly as well as add different parameter to columns like storing IN_MEMORY , Setting compression for particular column family , setting number of versions etc.

alter_status :
Get the status of the alter command. Indicates the number of regions of the table that have received the updated schema Pass table name.

alter_async :
Alter column family schema, does not wait for all regions to receive the
schema changes. Pass table name and a dictionary specifying new column
family schema. Dictionaries are described on the main help command output.
Dictionary must include name of column family to alter.
To change or add the ‘f1’ column family in table ‘t1’ from defaults
to instead keep a maximum of 5 cell VERSIONS, do:hbase> alter_async ‘t1’, NAME => ‘f1’, VERSIONS => 5To delete the ‘f1’ column family in table ‘t1’, do:

Elasticsearch Aggregation provides capability similar to RDBMS group by opeartor.
Facets provide a great way to aggregate data within a document set context. This context is defined by the executed query in combination with the different levels of filters that can be defined (filtered queries, top-level filters, and facet level filters). While powerful, their implementation is not designed from the ground up to support complex aggregations and is thus limited.
An aggregation can be seen as a unit-of-work that builds analytic information over a set of documents.
There are many different types of aggregation, each with it’s own purpose & output. To Better understand these type, It is often best to break down into 2 families.
1. Bucketing
– A family of aggregations that build buckets , where each bucket is associated with key and a document criterion
– When the aggregation is executed, all the buckets criteria are evaluated on every document in the context and when a criterion matches, the document is considered to “fall in” the relevant bucket
– By the end of the aggregation process, we’ll end up with a list of buckets – each one with a set of documents that “belong” to it.
2. Metric
– Aggregations that keep track and compute metrics over a set of documents.
Different kinds of aggregation is listed below:
1.Min Aggregation
2.Max Aggregation
3.Sum Aggregation
4.Avg Aggregation
5.Stats Aggregation
6.Extended Stats Aggregation
7.Value Count Aggregation
8.Percentiles Aggregation
9.Percentile Ranks Aggregation
10.Cardinality Aggregation
11.Geo Bounds Aggregation
12.Top hits Aggregation
13.Scripted Metric Aggregation
14.Global Aggregation
15.Filter Aggregation
16.Filters Aggregation
17.Missing Aggregation
18.Nested Aggregation
19.Reverse nested Aggregation
20.Children Aggregation
21.Terms Aggregation
22.Significant Terms Aggregation
23.Range Aggregation
24.Date Range Aggregation
25.IPv4 Range Aggregation
26.Histogram Aggregation
27.Date Histogram Aggregation
28.Geo Distance Aggregation
29.GeoHash grid Aggregation